Low-Light Image Enhancement is a computer vision task which intensifies the dark images to appropriate brightness. It can also be seen as an ill-posed problem in image restoration domain. With the success of deep neural networks, the convolutional neural networks surpass the traditional algorithm-based methods and become the mainstream in the computer vision area. To advance the performance of enhancement algorithms, we propose an image enhancement network (HWMNet) based on an improved hierarchical model: M-Net+. Specifically, we use a half wavelet attention block on M-Net+ to enrich the features from wavelet domain. Furthermore, our HWMNet has competitive performance results on two image enhancement datasets in terms of quantitative metrics and visual quality. The source code and pretrained model are available at https://github.com/FanChiMao/HWMNet.
@article{arxiv.2203.01296,
title = {Half Wavelet Attention on M-Net+ for Low-Light Image Enhancement},
author = {Chi-Mao Fan and Tsung-Jung Liu and Kuan-Hsien Liu},
journal= {arXiv preprint arXiv:2203.01296},
year = {2023}
}